ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results

This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop “Geometric and Topo- logical Representation Learning”. The competition asked participants to provide implementations of machine learning algorithms on manifolds that wo...

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Main Authors: Myers, Adele, Utpala, Saiteja, Talbar, Shubham, Sanborn, Sophia, Shewmake, Christian, Donnat, Claire, Mathe, Johan, Lupo, Umberto, Sonthalia, Rishi, Cui, Xinyue, Szwagier, Tom, Pignet, Arthur, Bergsson, Andri, Hauberg, Søren, Nielsen, Dmitriy, Sommer, Stefan, Klindt, David, Hermansen, Erik, Vaupel, Melvin, Dunn, Benjamin Adric, Xiong, Jeffrey, Aharony, Noga, Noga, Aharony, Pe’er, Itsik, Ambellan, Felix, Hanik, Martin, Nava-Yazdani, Esfandiar, von Tycowicz, Christoph, Miolane, Nina
Format: Article
Language:English
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Summary:This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop “Geometric and Topo- logical Representation Learning”. The competition asked participants to provide implementations of machine learning algorithms on manifolds that would respect the API of the open-source software Geomstats (manifold part) and Scikit-Learn (machine learning part) or PyTorch. The challenge attracted seven teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings.